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The fidelity of the hybrid electric vehicle simulation is increased with the integration of a computationally-efficient finite-element based electric machine model, in order to address optimization of component design for system level goals. In-wheel electric motors are considered because of the off-road military application which differs significantly from commercial HEV applications. Optimization framework is setup by coupling the vehicle simulation to the constrained optimization solver. Utilizing the increased design flexibility afforded by the model, the solver is able to reshape the electric machine's efficiency map to better match the vehicle operation points. As the result, the favorable design of the e-machine is selected to improve vehicle fuel economy and reduce cost, while satisfying performance constraints.

Vehicle structure crashworthiness design is one of the most challenging problems in product development and it has been studied for decades. Challenges still remain, which include developing a reliable and systematic approach for general crashworthiness design problems, which can be used to design an optimum vehicle structure in terms of topology, shape, and size, and for both structural layout and material layout. In this paper, an advanced and systematic approach is presented, which is called Magic Cube (MQ) approach for crashworthiness design. The proposed MQ approach consists of three major dimensions: Decomposition, Design Methodology, and General Considerations. The Decomposition dimension is related to the major approaches developed for the crashworthiness design problem, which has three layers: Time (Process) Decomposition, Space Decomposition, and Scale Decomposition.

Simultaneous measurements of the radial and the tangential components of velocity are obtained in a high-speed, direct-injection diesel engine typical of automotive applications. Results are presented for engine operation with fuel injection, but without combustion, for three different swirl ratios and four injection pressures. With the mean and fluctuating velocities, the r-θ plane shear stress and the mean flow gradients are obtained. Longitudinal and transverse length scales are also estimated via Taylor's hypothesis. The flow is shown to be sufficiently homogeneous and stationary to obtain meaningful length scale estimates. Concurrently, the flow and injection processes are simulated with KIVA-3V employing a RNG k-ε turbulence model. The measured turbulent kinetic energy k, r-θ plane mean strain rates ( 〈Srθ〉, 〈Srr〉, and 〈Sθθ〉 ), deviatoric turbulent stresses , and the r-θ plane turbulence production terms are compared directly to the simulated results.

This paper investigates the effects of manufacturing variations in fuel injectors on the engine performance with emphasis on emissions. The variations are taken into consideration within a Reliability-Based Design Optimization (RBDO) framework. A reduced version of Multi-Zone Diesel engine Simulation (MZDS), MZDS-lite, is used to enable the optimization study. The numerical noise of MZDS-lite prohibits the use of gradient-based optimization methods. Therefore, surrogate models are developed to filter out the noise and to reduce computational cost. Three multi-objective optimization problems are formulated, solved and compared: deterministic optimization using MZDS-lite, deterministic optimization using surrogate models and RBDO using surrogate models. The obtained results confirm that manufacturing variation effects must be taken into account in the early product development stages.

In FOA (First Order Analysis) any vehicle body structure might be interpreted as a collective simple structure that can be decomposed into 3 fundamental structure types. The first structure is the “BEAM”, whose cross sectional properties as well as its material dominates the mechanical behavior, the second is the “PANEL (shear panel, plate, and shell)”, whose mechanical behavior can be varied by changing its geometrical properties in the thickness direction, i.e. adding beads or flanges. The third structure is the “JOINT”, which connects the proceeding structures, and transfer complex three-dimensional loads with three-dimensional deformation. In the present work, we shall propose a methodology to identify a portion of an arbitrary FE model of an automotive body structure, with a “BEAM” structure in the FOA approach. In the latter chapter of this paper, cross section loads will be related with cross sectional properties in the aspect of the element strain energy concept.

The need for improved understanding of new magnesium alloys for the automotive industry continues to grow as the application for these lightweight alloys expands to more demanding environments, particularly in drivetrain components. Their use at elevated temperatures, such as in transmission cases, presents a challenge because magnesium alloys generally have lower creep resistance than aluminum alloys currently employed for such applications. In this study, a new die cast magnesium alloy, MEZ, containing rare earth (RE) elements and zinc as principal alloying constituents, was examined for its bolt-load retention (BLR) properties. Preloads varied from 14 to 28 kN and test temperatures ranged from 125 to 175°C. At all test temperatures and preloads, MEZ retained the greatest fraction of the initial imposed preload when compared to the magnesium alloys AZ91D, AE42, AM50, and the AM50+Ca series alloys.

New high-temperature Mg alloys are being considered to replace 380 Al in transmission cases, wherein bolt-load retention, and creep, is of prime concern. One of these alloys is die cast AE42, which has much better creep properties than does AZ91D but is still not as creep resistant as 380 Al. It is thus important to investigate bolt-load retention and creep of AE42 as an initial step in assessing its suitability as a material for transmission housings. To that end, the bolt-load retention behavior of die-cast AE42, AZ91D and 380 Al have been examined using standard M10 bolts specially instrumented with stable high-temperature strain gages. The bolt-load retention test pieces were die cast in geometries approximating the flange and boss regions in typical bolted joints. Bolt-load retention properties were examined as a function of time (at least 100 hours), temperature (150 and 175 °C) and initial bolt preload (14 to 34 kN).

A numerical study has been conducted to investigate possible extension of the low load limit of the HCCI operating range by charge stratification using direct injection. A wide range of SOI timings at a low load HCCI engine operating condition were numerically examined to investigate the effect of DI. A multidimensional CFD code KIVA3v with a turbulent combustion model based on a modified flamelet approach was used for the numerical study. The CFD code was validated against experimental data by comparing pressure traces at different SOI’s. A parametric study on the effect of SOI on combustion has been carried out using the validated code. Two parameters, the combustion efficiency and CO emissions, were chosen to examine the effect of SOI on combustion, which showed good agreement between numerical results and experiments. Analysis of the in-cylinder flow field was carried out to identify the source of CO emissions at various SOI’s.

The power management control system development and vehicle test results for a medium-duty hybrid electric truck are reported in this paper. The design procedure adopted is a model-based approach, and is based on the dynamic programming technique. A vehicle model is first developed, and the optimal control actions to maximize fuel economy are then obtained by the dynamic programming method. A near-optimal control strategy is subsequently extracted and implemented using a rapid-prototyping control development system, which provides a convenient environment to adjust the control algorithms and accommodate various I/O configurations. Dynamometer-testing results confirm that the proposed algorithm helps the prototype hybrid truck to achieve a 45% fuel economy improvement on the benchmark (non-hybrid) vehicle. It also compares favorably to a conventional rule-based control method, which only achieves a 31% fuel economy improvement on the same hybrid vehicle.

A complete scheme for motion prediction based on motion capture data is presented. The scheme rests on three main components: a special posture representation, a diverse motion capture database and prediction method. Most prior motion prediction schemes have been based on posture representations based on well-known local or global angles. Difficulties have arisen when trying to satisfy constraints, such as placing a hand on a target or scaling the posture for a subject of different stature. Inverse kinematic methods based on such angles require optimization that become increasingly complex and computationally intensive for longer linkages. A different representation called stretch pivot coordinates is presented that avoids these difficulties. The representation allows for easy rescaling for stature and other linkage length variations and satisfaction of endpoint constraints, all without optimization allowing for rapid real time use.

Model Predictive Control (MPC) design methods are becoming popular among automotive control researchers because they explicitly address an important challenge faced by today’s control designers: How does one realize the full performance potential of complex multi-input, multi-output automotive systems while satisfying critical output, state and actuator constraints? Nonlinear MPC (NMPC) offers the potential to further improve performance and streamline the development for those systems in which the dynamics are strongly nonlinear. These benefits are achieved in the MPC framework by using an on-line model of the controlled system to generate the control sequence that is the solution of a constrained optimization problem over a receding horizon.

Existing methods for design optimization under uncertainty assume that a high level of information is available, typically in the form of data. In reality, however, insufficient data prevents correct inference of probability distributions, membership functions, or interval ranges. In this article we use an engine design example to show that optimal design decisions and reliability estimations depend strongly on uncertainty characterization. We contrast the reliability-based optimal designs to the ones obtained using worst-case optimization, and ask the question of how to obtain non-conservative designs with incomplete uncertainty information. We propose an answer to this question through the use of Bayesian statistics. We estimate the truck's engine reliability based only on available samples, and demonstrate that the accuracy of our estimates increases as more samples become available.

This study presents an efficient process to optimize the transmission loss of a vehicle muffler by using both experimental and analytical methods. Two production mufflers were selected for this study. Both mufflers have complex partitions and one of them was filled with absorbent fiberglass. CAD files of the mufflers were established for developing FEA models in ANSYS and another commercial software program (CFEA). FEA models were validated by experimental measurements using a two-source method. After the models were verified, sensitivity studies of design parameters were performed to optimize the transmission loss (TL) of both mufflers. The sensitivity study includes the perforated hole variations, partition variations and absorbent material insertion. The experimental and sensitivity analysis results are included in the paper.

A new method for crashworthiness optimization of vehicle structures is presented, where an early design exploration is done by the optimization of an equivalent mechanism approximating a vehicle structure. An equivalent mechanism (EM) is a network of rigid bodies connected by prismatic and revolute joints with special nonlinear springs. These springs are designed to mimic the force-displacement characteristics of thin-walled beams often found in the vehicle body structures. A computer software is implemented that allows the designer to quickly construct an equivalent mechanism model of a structure using a graphical user interface (GUI) to optimize the model for given objectives prior to final tuning using finite element (FE) models. A case study of a vehicle front substructure consisting of mid and lower rails is presented, which demonstrates that the new approach can obtain a better design with less computational resources than the direct optimization of a FE model.

This paper proposes a framework to perform design optimization of a series PHEV and investigates the impact of using real-world driving inputs on final design. Real-World driving is characterized from a database of naturalistic driving generated in Field Operational Tests. The procedure utilizes Markov chains to generate synthetic drive cycles representative of real-world driving. Subsequently, PHEV optimization is performed in two steps. First the optimal battery and motor sizes to most efficiently achieve a desired All Electric Range (AER) are determined. A synthetic cycle representative of driving over a given range is used for function evaluations. Then, the optimal engine size is obtained by considering fuel economy in the charge sustaining (CS) mode. The higher power/energy demands of real-world cycles lead to PHEV designs with substantially larger batteries and engines than those developed using repetitions of the federal urban cycle (UDDS).

A method is presented here that detects aircraft tail surface icing that might normally be unobserved by the flight crew. Such icing can be detected through the action of highly computationally efficient signal processing of existing sensor signals using a so-called failure detection filter (FDF). The FDF creates a unique output signature permitting relatively early detection of tail surface icing. The FDF incorporates a stable state estimator from which the icing signature is created. This estimator is robust to analytical modeling errors or uncertainties, and to process noise (e.g. turbulence). Excellent performance of the method is demonstrated via simulation.

The shape and topology optimization method using a homogenization method is a powerful design tool because it can treat topological changes of a design domain. This method was originally developed in 1988 [1] and have been studied by many researchers. However, their scope of application in real vehicle design works has been limited where a design domain and boundary conditions are very complicated. The authors have developed a powerful optimization system by adopting a general purpose finite element analysis code. A method for treating vibration problems is also discussed. A new objective function corresponding to a multi-eigenvalue optimization problem is suggested. An improved optimization algorithm is then applied to solve the problem. Applications of the optimization system to design the body and the parts of a solar car are presented.

Improving the performance characteristics of a typical disc brake encompasses a number of design strategies as well as limitations imposed by cost objectives. Utilizing pad loading uniformity in a design is one strategy that offers an improvement in desired performance characteristics, including a reduction in tapered lining wear as well as a possible reduced propensity for noise generation. To approach this design strategy, a procedure has been developed to tailor the brake pad lining profile to maximize pad loading uniformity in a multibody dynamics model of a typical disc brake. In determining an optimal lining configuration, a suitable compromise for gaining beneficial performance improvements in a cost effective manner is reached. The implementation of this design strategy involves the parametric definition of the lining profile by introducing a series of variables that are linked to the profile markers.

Engaging in visual-manual tasks such as selecting a radio station, adjusting the interior temperature, or setting an automation function can be distracting to drivers. Additionally, if setting the automation fails, driver takeover can be delayed. Traditionally, assessing the usability of driver interfaces and determining if they are unacceptably distracting (per the NHTSA driver distraction guidelines and SAE J2364) involves human subject testing, which is expensive and time-consuming. However, most vehicle engineering decisions are based on computational analyses, such as the task time predictions in SAE J2365. Unfortunately, J2365 was developed before touch screens were common in motor vehicles.

The aluminum alloy 7075-T6 has the potential to be used for structural automotive body components as an alternative to boron steel. Although this alloy shows poor formability at room temperature, it has been demonstrated that hot stamping is a feasible sheet metal process that can be used to overcome the forming issues. Hot stamping is an elevated temperature forming operation in which a hot blank is formed and quenched within a stamping die. Attaining a high quench rate is a critical step of the hot stamping process and corresponds to maximum strength and corrosion resistance. This work looks at measuring the quench rate of AA7075-T6 by way of three different approaches: water, a water-cooled plate, and a bead die. The water-cooled plate and the bead die are laboratory-scale experimental setups designed to replicate the hot stamping/die quenching process.